# frbs v3.1-0

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## Fuzzy Rule-Based Systems for Classification and Regression Tasks

An implementation of various learning algorithms based on fuzzy rule-based systems (FRBSs) for dealing with classification and regression tasks. Moreover, it allows to construct an FRBS model defined by human experts. FRBSs are based on the concept of fuzzy sets, proposed by Zadeh in 1965, which aims at representing the reasoning of human experts in a set of IF-THEN rules, to handle real-life problems in, e.g., control, prediction and inference, data mining, bioinformatics data processing, and robotics. FRBSs are also known as fuzzy inference systems and fuzzy models. During the modeling of an FRBS, there are two important steps that need to be conducted: structure identification and parameter estimation. Nowadays, there exists a wide variety of algorithms to generate fuzzy IF-THEN rules automatically from numerical data, covering both steps. Approaches that have been used in the past are, e.g., heuristic procedures, neuro-fuzzy techniques, clustering methods, genetic algorithms, squares methods, etc. Furthermore, in this version we provide a universal framework named 'frbsPMML', which is adopted from the Predictive Model Markup Language (PMML), for representing FRBS models. PMML is an XML-based language to provide a standard for describing models produced by data mining and machine learning algorithms. Therefore, we are allowed to export and import an FRBS model to/from 'frbsPMML'. Finally, this package aims to implement the most widely used standard procedures, thus offering a standard package for FRBS modeling to the R community.

## Functions in frbs

 Name Description FRBCS.eng FRBCS: prediction phase frbsPMML The frbsPMML generator rulebase The rule checking function DM.update FIR.DM updating function ANFIS ANFIS model building FRBCS.CHI FRBCS.CHI model building GFS.GCCL GFS.GCCL model building denorm.data The data de-normalization GFS.LT.RS.test GFS.LT.RS: The prediction phase summary.frbs The summary function for frbs objects FIR.DM FIR.DM model building frbs.eng The prediction phase HGD.update FS.HGD updating function ANFIS.update ANFIS updating function defuzzifier Defuzzifier to transform from linguistic terms to crisp values data.gen3d A data generator predict.frbs The frbs prediction stage GFS.GCCL.eng GFS.GCCL.test: The prediction phase ECM Evolving Clustering Method FS.HGD FS.HGD model building GFS.Thrift GFS.Thrift model building GFS.FR.MOGUL GFS.FR.MOGUL model building frbs-package Getting started with the frbs package FRBCS.W FRBCS.W model building FH.GBML FH.GBML model building WM WM model building GFS.LT.RS GFS.LT.RS model building SLAVE.test SLAVE.test: The prediction phase inference The process of fuzzy reasoning GFS.FR.MOGUL.test GFS.FR.MOGUL: The prediction phase SBC The subtractive clustering and fuzzy c-means (SBC) model building read.frbsPMML The frbsPMML reader frbsObjectFactory The object factory for frbs objects fuzzifier Transforming from crisp set into linguistic terms SLAVE SLAVE model building frbsData Data set of the package frbs.gen The frbs model generator DENFIS DENFIS model building plotMF The plotting function norm.data The data normalization HyFIS HyFIS model building SBC.test SBC prediction phase GFS.Thrift.test GFS.Thrift: The prediction phase write.frbsPMML The frbsPMML writer DENFIS.eng DENFIS prediction function HyFIS.update HyFIS updating function frbs.learn The frbs model building function No Results!